Analog VLSI Circuit Implementation of an Adaptive Neuromorphic Olfaction Chip

Koickal, Thomas Jacob; Hamilton, Alister; Tan, Su Lim; Covington, James A.; Gardner, Julian W.; Pearce, Tim

doi:10.1109/tcsi.2006.888677

Cited by 130 publications

(82 citation statements)

References 36 publications

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“…Another approach to neural modeling is via direct emulation of neuronal dynamics on electronic devices such as complementary metal-oxide-semiconductor (CMOS) (27)(28)(29) or nanowire circuits (30)(31)(32); i.e., analog "neuromorphic" computation instead of digital model simulation. Recently, there has been growing interest in the neuromorphic modeling and implementation of the STDP learning rule using CMOS (32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42) or metal-oxide-metal circuits (43) or memristor-based nanodevices. Compared to conventional software-based computer modeling and simulation approaches, these neuromorphic electronic circuits have extremely small size (micro-to nanoscale) and low power requirements (μA to pA current per unit device with 0.5-5 V power supply) for large scale neural modeling and high speed simulation purposes.…”

mentioning

confidence: 99%

A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity

Rachmuth

Shouval²,

Bear

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

178

149

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Current advances in neuromorphic engineering have made it possible to emulate complex neuronal ion channel and intracellular ionic dynamics in real time using highly compact and powerefficient complementary metal-oxide-semiconductor (CMOS) analog very-large-scale-integrated circuit technology. Recently, there has been growing interest in the neuromorphic emulation of the spike-timing-dependent plasticity (STDP) Hebbian learning rule by phenomenological modeling using CMOS, memristor or other analog devices. Here, we propose a CMOS circuit implementation of a biophysically grounded neuromorphic (iono-neuromorphic) model of synaptic plasticity that is capable of capturing both the spike rate-dependent plasticity (SRDP, of the Bienenstock-Cooper-Munro or BCM type) and STDP rules. The iono-neuromorphic model reproduces bidirectional synaptic changes with NMDA receptor-dependent and intracellular calcium-mediated long-term potentiation or long-term depression assuming retrograde endocannabinoid signaling as a second coincidence detector. Changes in excitatory or inhibitory synaptic weights are registered and stored in a nonvolatile and compact digital format analogous to the discrete insertion and removal of AMPA or GABA receptor channels. The versatile Hebbian synapse device is applicable to a variety of neuroprosthesis, brain-machine interface, neurorobotics, neuromimetic computation, machine learning, and neural-inspired adaptive control problems.iono-neuromorphic modeling | rate-based synaptic plasticity | silicon neuron | subthreshold microelectronics | VLSI circuit L earning and memory are emergent animal behaviors governed by activity-dependent neuronal adaptation rules in response to changing environments. A putative neuronal mechanism of learning and memory is Hebbian synaptic plasticity (1)-the adaptive modification of excitatory synaptic strength following paired activation of the pre-and postsynaptic neurons. Two classic paradigms for the induction of Hebbian synaptic plasticity in the mammalian hippocampus and neocortex are rate-based plasticity (2-4) [herein referred to as spike-rate-dependent plasticity (SRDP)] and spike-timing-dependent plasticity (STDP) (5-7). The SRDP induction protocols control presynaptic firing rate in order to vary the sign and magnitude of synaptic plasticity (8): a high-frequency (20-100 Hz) train of presynaptic pulses results in long-term potentiation (LTP) of the synaptic strength, whereas a low-frequency (1-5 Hz) train results in long-term depression (LTD). These protocols are consistent with the theoretical learning rule (BCM rule) proposed by Bienenstock, Cooper, and Munro (9), in which the sign and magnitude of synaptic plasticity are controlled solely by postsynaptic activity as determined by presynaptic firing rate: low postsynaptic activity weakens synaptic efficacy and high postsynaptic activity strengthens it. By contrast, the STDP induction protocol stipulates that precise timing of preand postsynaptic activities determines the direction and strength of synaptic pl...

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mentioning

confidence: 99%

A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity

Rachmuth

Shouval²,

Bear

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

178

149

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“…These engineering limitations may find solutions through introducing this biologically-plausible model to eNose for signal processing. This idea has been used on a single VLSI olfactory chip to effectively detect odors [30] .…”

Section: Figurementioning

confidence: 99%

Progress in bionic information processing techniques for an electronic nose based on olfactory models

Zheng

2009

Chin. Sci. Bull.

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As a novel bionic analytical technique, an electronic nose, inspired by the mechanism of the biological olfactory system and integrated with modern sensing technology, electronic technology and pattern recognition technology, has been widely used in many areas. Moreover, recent basic research findings in biological olfaction combined with computational neuroscience promote its development both in methodology and application. In this review, the basic information processing principle of biological olfaction and artificial olfaction are summarized and compared, and four olfactory models and their applications to electronic noses are presented. Finally, a chaotic olfactory neural network is detailed and the utilization of several biologically oriented learning rules and its spatiotemporal dynamic propties for electronic noses are discussed. The integration of various phenomena and their mechanisms for biological olfaction into an electronic nose context for information processing will not only make them more bionic, but also perform better than conventional methods. However, many problems still remain, which should be solved by further cooperation between theorists and engineers.artificial olfaction, olfactory model, pattern recognition, electronic nose, bionics Various sensors derive different types of information from complicated environments, following which computers analyze before actuators perform particular tasks. To a certain degree, such devices have extended human sensory, brain and physical power to enhance the capacity of understanding and transforming nature. However, biological systems derived from evolution are useful models for engineering design, particularly with regard to bionics.Electronic noses (eNose) are a kind of novel bionic analytical instrument for mimicking the principle of biological olfaction. Generally, an electronic nose consists of an array of cross-sensitive gas sensors and an appropriate pattern recognition method, capable of automatically detecting and discriminating simple or complex odors [1] . Only twenty years after Persaud et al.'s pioneer work [2] , eNoses have been widely utilized within the food industry, for environmental monitoring, public security and medical diagnosis [3,4] , as they tend to be relatively fast, easy to use and objective with a modest overall cost.As a multidisciplinary research field, most studies of eNose primarily focus on gas sensor technology [5] and pattern recognition methods [6] . Pattern recognition models remain inadequately investigated such that they are the bottle-neck for commercialization. With more novel sensing techniques are applied to eNoses [3,7] , signal processing and pattern recognition methods have increased in significance. The information processing mechanism in olfaction from the genetic, cellular to systemic levels is more fully understand as a result of cross-

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“…Traditionally, the subthreshold voltage-current dependence of CMOS transistors has been used to generate an exponential current waveform based on a linear (saw-tooth) voltage [2]. The OTA-C approach uses a transconductance to emulate an RC decay [3], [4]. In deep submicron technologies, solutions have to be found that rely less on analog performance, such as a switched capacitor charge sharing emulating an exponential decay [5].…”

Section: Introductionmentioning

confidence: 99%

A fixed point exponential function accelerator for a neuromorphic many-core system

Partzsch

Höppner

Eberlein

et al. 2017

2017 IEEE International Symposium on Circuits and Systems (ISCAS)

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Abstract-Many models of spiking neural networks heavily rely on exponential waveforms. On neuromorphic multiprocessor systems like SpiNNaker, they have to be approximated by dedicated algorithms, often dominating the processing load. Here we present a processor extension for fast calculation of exponentials, aimed at integration in the next-generation SpiNNaker system. Our implementation achieves single-LSB precision in a 32bit fixed-point format and 250Mexp/s throughput at 0.44nJ/exp for nominal supply (1.0V), or 0.21nJ/exp at 0.7V supply and 77Mexp/s, demonstrating a throughput multiplication of almost 50 and 98% energy reduction at 2% area overhead per processor on a 28nm CMOS chip.

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Analog VLSI Circuit Implementation of an Adaptive Neuromorphic Olfaction Chip

Cited by 130 publications

References 36 publications

A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity

A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity

Progress in bionic information processing techniques for an electronic nose based on olfactory models

A fixed point exponential function accelerator for a neuromorphic many-core system

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